Predictive Analytics on Student Academics

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-34 Number-2
Year of Publication : 2016
Authors : Mr. K. Balaprasath, Mr. L. Arun Raj
  10.14445/22312803/IJCTT-V34P116

MLA

Mr. K. Balaprasath, Mr. L. Arun Raj "Predictive Analytics on Student Academics". International Journal of Computer Trends and Technology (IJCTT) V34(2):93-97, April 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Academic performances among university students are the topic of interest in educational society. The students performance plays a significant role for the course discontinuation. A large set of academic data is used for predicting the students yearly to fulfil the degree requirements. Two data processing algorithms have been used K-Means clustering and Apriori combined with Linear Regression are applied. The proposed system is to predict the learning concert of the learners supported both academic and non academic records. Data collected from the students via Google Forms are analyzed using the mining algorithms and the results are displayed using a visualization tool. Based on the analysis the academic performance of the student could be evaluated, thereby initiating steps to enhance the teaching learning process.

References
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Keywords
Educational Data Mining (EDM), Kmeans Clustering, Apriori, Linear Regression.